计算机科学

计算机科学 ›› 2022, Vol. 49 ›› Issue (8): 178-183.doi: 10.11896/jsjkx.210600066

• 计算机图形学& 多媒体 • 上一篇    下一篇

高分辨率斜视聚束SAR回波仿真加速算法研究

郭拯危, 付泽文, 李宁, 白澜   

  1. 河南大学计算机与信息工程学院 河南 开封 475004
    河南大学河南省智能技术与应用工程技术研究中心 河南 开封 475004
    河南大学河南省大数据分析与处理重点实验室 河南 开封 475004
  • 收稿日期:2021-06-04 修回日期:2021-09-06 发布日期:2022-08-02
  • 通讯作者: 李宁(hedalining@henu.edu.cn)
  • 作者简介:(gzw@henu.edu.cn)
  • 基金资助:
    国家自然科学基金(61871175);河南省高等学校重点科研项目(19A420005,21A520004);河南省科技攻关计划项目(202102210175,212102210093,212102210101);自然资源部国土卫星遥感应用重点实验室经费资助项目(KLSMNR-202102)

Study on Acceleration Algorithm for Raw Data Simulation of High Resolution Squint Spotlight SAR

GUO Zheng-wei, FU Ze-wen, LI Ning, BAI Lan   

  1. College of Computer and Information Engineering,Henan University,Kaifeng,Henan 475004,China
    Henan Engineering Research Center of Intelligent Technology and Application,Henan University,Kaifeng,Henan 475004,China
    Key Laboratory of Analysis and Processing on Big Data of Henan Province,Henan University,Kaifeng,Henan 475004,China
  • Received:2021-06-04 Revised:2021-09-06 Published:2022-08-02
  • About author:GUO Zheng-wei,born in 1963,bachelor,professor,master supervisor.is a member of China Computer Federation.Her main research interests include SAR image processing techniques,and SAR image application of ecological environment.
    LI Ning,born in 1987,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include multi-mode SAR imaging and SAR application.
  • Supported by:
    National Natural Science Foundation of China(61871175),College Key Research Project of Henan Province(19A420005,21A520004),Plan of Science and Technology of Henan Province(202102210175,212102210093 ,212102210101) and Key Laboratory of Land Satellite Remote Sensing Application,Ministry of Natural Resources of the People’s Republic of China(KLSMNR-202102).

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摘要： 回波仿真是合成孔径雷达系统开发的前端工作,具有重要意义。针对高分辨率斜视聚束SAR,一般采用时域回波仿真的方法,但是其仿真效率过慢。为了高效实现高分辨率斜视聚束SAR的回波仿真,提出了一种有效的加速算法。结合斜视聚束SAR时域回波模型及其信号特性,对回波仿真过程中存在的距离徙动进行补偿,以减少冗余计算量并节省内存空间;采用数据自适应分块的方法,在图形处理器中分别计算分块后的子数据,利用GPU强大的计算能力进行加速;将子数据块进行传输,在内存中拼接。该算法提高了时域回波仿真的计算效率,解决了数据量大、GPU显存有限且显存与内存之间数据传输速度较慢的问题。点目标和面目标仿真的实验结果表明,该算法的加速比达到了219.8,验证了所提方法的有效性。

关键词: 高分辨率, 合成孔径雷达, 距离徙动, 时域回波仿真, 图像处理器

Abstract: Raw data simulation is the front-end work of synthetic aperture radar(SAR) system development,which is of great significance.For high resolution squint spotlight SAR,time-domain raw data simulation is usually used,but its simulation efficiency is very low.In order to realize the raw data simulation of high resolution squint spotlight SAR efficiently,an effective acceleration algorithm is proposed.To reduce the redundant computation and save memory,this algorithm combines the time-domain raw data simulation model and its signal characteristics to compensate the range cell migration(RCM) in the raw data simulation process of squint spotlight SAR.An adaptive data partitioning algorithm is adopted to compute the partitioned data in graphic processing unit(GPU), and the powerful computing capabilities of GPU is used to improve efficiency.Then the sub data blocks are transmitted and spliced in memory.The proposed algorithm improves the computational efficiency of time-domain raw data simulation,and solves the problems of huge volume of raw data,limited GPU memory and data transmission between video memory and memory.Experimental results of point targets and distributed targets show that the speedup ratio of this algorithm reaches 219.8,which verifies the effectiveness of the proposed method.

Key words: GPU, High resolution, Range cell migration, Synthetic aperture radar, Time-domain raw data simulation

中图分类号: 

  • TP702
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